In this paper, the author proposes the application of a genetic algorithm and simulated annealing to solve the network planning problem. Compared with other optimisation methods, genetic algorithm and simulated annealing are suitable for traversing large search spaces since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will ...

This paper attempts to overcome the local convergence problem of the Expectation Maximization (EM) based training of the Hidden Markov Model (HMM) in speech recognition. We propose a hybrid algorithm, Simulated Annealing Stochastic version of EM (SASEM), combining Simulated Annealing with EM that reformulates the HMM estimation process using a stochastic step between the EM steps and the SA. Th...

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous...

In this paper, a new hybrid of genetic algorithm (GA) and simulated annealing (SA), referred to as GSA, is presented. In this algorithm, SA is incorporated into GA to escape from local optima. The concept of hierarchical parallel GA is employed to parallelize GSA for the optimization of multimodal functions. In addition, multi-niche crowding is used to maintain the diversity in the population o...

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated A...

To solve premature phenomenon and falling into local optimum of genetic algorithm, the simulated annealing algorithm is introduced to the genetic algorithm and a simulated annealing is presented based on genetic clustering algorithm, a new effective SA, crossover operator and mutation operator proposed for fitting the partition-based chromosome coding. In addition, the Euclidean distance is rep...

This paper proposes Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). In this algorithm, there are several processes of Simulated Annealing (SA) working parallel. To exchange information between the solutions, the operation of genetic crossover is performed. Through the continuous test problems, it is found that PSA/GAc can search the solution effectively. The proposed algorithm i...

Earth dam is a structure as homogeneous or non-homogeneous forms for raising water level or water supply. Earth dam consist of different parts that one of the main parts is clay core. Choosing an optimal non permeable core which causes reduction of seepage through dam body and also being stable is necessary. The objective of this research is to optimize the geometry of earth dam clay core such ...

bilevel programming, a tool for modeling decentralized decision problems, consists of the objective of the leader at its first level and that of the follower at the second level. bilevel programming has been proved to be an np-hard problem. numerous algorithms have been developed for solving bilevel programming problems. these algorithms lack the required efficiency for solving a real problem. ...

We have developed a hybrid optimization algorithm for inversion of gravity data using a binary formulation. The new algorithm utilizes the Genetic Algorithm (GA) as a global search tool, while implementing Quenched Simulated Annealing (QSA) intermittently for local search. The hybrid has significantly decreased computational cost over GA or Simulated Annealing (SA) alone and has allowed for suc...